Accepted for/Published in: JMIR Biomedical Engineering
Date Submitted: Jun 2, 2020
Open Peer Review Period: Jun 2, 2020 - Jun 22, 2020
Date Accepted: Aug 21, 2020
(closed for review but you can still tweet)
Feature Extraction and Genetic Algorithm based Feature Selection for Diagnosis of Type-2 Diabetes using Electrogastrograms
ABSTRACT
Background:
Electrogastrography (EGG) is a non-invasive electrophysiological measurement procedure followed to measure the frequency and promptness of gastric myoelectrical activity, which is normally considered to investigate the mechanisms of human digestive system. Diabetes can cause alterations in the process of digestion.
Objective:
The objective of this work is to extract and select potential informative features from recorded normal and diabetic electrogastrograms for diagnosis of diabetes using EGG signals.
Methods:
In this work, a total of thirty features of electrogastrograms measured from normal subjects and diabetic cases, were extracted. Further, twenty potential informative features were selected using Genetic Algorithm assisted feature selection process. The extracted features were analyzed for further classification of normal and diabetic EGG signals.
Results:
Results demonstrate that there are distinct variations between the EMG signals recorded from normal subjects and diabetic patients. The investigations reveal that the features namely Maragos fractal dimension and Hausdorff’s box-counting fractal dimension have high degree of correlation with the mobility of normal and diabetic electrogastrograms.
Conclusions:
Based on the analysis on the extracted features, it is seen that the selected features are suitable for the design of automated classification systems for classification of normal and diabetic cases.
Citation
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Copyright
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